• Journal of the Korea Society of Computer and Information
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• v.18 no.11
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• pp.13-21
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• 2013

The goal of this paper is to provide performance improvement and stability for satellite data correction of some distortions due to cloud or radiance through distributed processing on cluster. To do this, we proposed and implemented SGE(Sun Grid Engine) based distributed processing methods using local storages and a status table. In the verification, the experiment result revealed that the proposed system on seven nodes improved the processing speed by 138.81% as compare to the existing system and provided good stability as well. This result showed that the proposed distributed processing work is more appropriate to process CPU bound jobs than I/O bound jobs. We expect that the proposed system will give scientists improved analysis performance in various fields and near-real time analysis services.

• Journal of the Korean Society of Marine Environment & Safety
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• v.10 no.1 s.20
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• pp.1-7
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• 2004

RADARSAT is one of many possible data sources that can play an important role in marine surveillance including ship detection because radar sensors have the two primary advantages: all-weather and day or night imaging. However, atmospheric effects on SAR imaging can not be bypassed and any remote sensing image has various geometric distortions, In this study, radiometric and geometric calibrations for RADARSAT/SAT data are tried using SGX products georeferenced as level 1. Even comparison of the near vs. far range sections of the same images requires such calibration Radiometric calibration is performed by compensating for effects of local illuminated area and incidence angle on the local backscatter, Conversion method of the pixel DNs to beta nought and sigma nought is also investigated. Finally, automatic geometric calibration based on the 4 pixels from the header file is compared to a marine chart. The errors for latitude and longitude directions are 300m and 260m, respectively. It can be concluded that the error extent is acceptable for an application to open sea and can be calibrated using a ground control point.

• Journal of Broadcast Engineering
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• v.21 no.1
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• pp.43-50
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• 2016

In this paper, we present a distortion correction for wearable Augmented Reality (AR) on mobile phones. Head Mounted Display (HMD) using mobile phones, such as Samsung Gear VR or Google's cardboard, introduces lens distortion of the rendered image to user. Especially, in case of AR the distortion is more complicated due to the duplicated optical systems from mobile phone's camera and HMD's lens. Furthermore, such distortions generate mismatches of the visual cognition or perception of the user. In a natural way, we can assume that transparent wearable displays are the ultimate visual system which generates the least misperception. Therefore, the image from the mobile phone must be corrected to cancel this distortion to make transparent-like AR display with mobile phone based HMD. We developed a transparent-like display in the mobile wearable AR environment focusing on two issues: pincushion distortion and field-of view. We implemented our technique and evaluated their performance.

• Journal of Broadcast Engineering
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• v.17 no.5
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• pp.851-863
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• 2012

Retinex algorithm enhances the contrast of image through visibility improvement. However, the conventional Retinex methods may produces color distortions due to error of hue representation and over-saturation since the methods work in RGB color space. In this paper, we propose a new Retinex algorithm with color correction, which improves contrast by using MSR(Multi-Scale Retinex) working in YCbCr color space and adaptively compensates the color saturation based on the maximum color difference table. Our algorithm maps the color difference signals to the correct gamut to prevent over-saturation phenomenon by considering the correlation between luminance and hue dependent saturation. Simulations results show that the proposed method gives better color improvement compared to the conventional methods.

• Journal of International Academy of Physical Therapy Research
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• v.4 no.2
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• pp.573-587
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• 2013

The purpose of this study was conducted in order to analyze the effects of the manual intervention and self-corrective exercise models of general coordinative manipulation(GCM) on the balance restoration of spine & extremities joints with distortions and mal-alignment areas. The subjects were the members who visited GCM Musculoskeletal Prevent Exercise Center from March 1 2012 to December 31 2013 because of spine & extremities joints distortion and mal-alignments, poor posture, and body type correction. All subjects were diagnosed with the four types of the GBT diagnosis. And according to the standards of the mobility vs stability types of the upper & lower body, they were classified into Group 1(40 persons) and Group 2(24 persons). For every other day for three times a week, GCM intervention models were applied to all subjects for four weeks, adding up to 12 times in total. Then the balance restoration effects were re-evaluated with the same methods. The results are as follows. 1) Balance restoration effects of VASdp(Visual analysis scale pain & discomfort) and ER(Equilibrium reaction: ER) came out higher in GCM body type(GBT) II III IV of Group 1. 2) In case of balance restoration effects in Moire and postural evaluation areas, Group 1 was higher and cervical and scapular girdle were higher in Group 2. The balance restoration of the four GBT types was significant in all regions(p<.05), and the scapular girdle came out as high in the order of GBTII IV I. 3) In case of thoracic-lumbar scoliosis and head rotation facial asymmetric cervical scoliosis ribcage forward, the balance restoration effects of the upper body postural evaluation areas came out the highest in Group 1 and Group 2, respectively. The balance restoration effects of the four GBT types were significant in all regions(p<.05), and came out the highest in lumbar scoliosis GBTIII I, ribcage forward and thoracic scoliosis GBTII IV. 4) The balance restoration effects of the lower body postural evaluation areas came out higher in Group 1 and Group 2 for pelvis girdle deviation patella high umbilicus tilt and hallux valgus foot longitudinal arch: FLA patella direction, respectively. The balance restoration effects of the four GBT types were significant in all regions(p<.05), and came out the highest in pelvis girdle deviation GBTIII I and patella high-direction GBTIV II I. 5) The balance restoration effects between the same GBT came out significant (p<.05) in all evaluation areas and items. The conclusions of this study was the manual intervention and self-corrective exercise models of the GCM about the mal-alignment of the spine & extremities joints across the whole body indicated high balance restoration effects(p<.05) in spine & extremities joints in all evaluation areas.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.2
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• pp.35-45
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• 2010

The objective of this study is to verify the positional accuracy by performing the orthometric corrections on the high resolution satellite images and to analyze the band correlation between the high resolution images corrected with orthometric correction. The objectives also included an analysis on the correlation of NDVI. For the orthometric correction of images from KOMPSAT2 and IKONOS, systematic errors were removed in use of RPC data, and non-planar distortions were corrected with GPS surveying data. Also, by preempting the image points at the same positions within ortho images, a comparison was performed on positional accuracies between image points of each image and GPS surveying points. The comparison was also made on the positional accuracies of image points. between the images. For correlation of band and correlation of NDVI, the descriptive statistics of DN values were acquired for respective bands by adding the Quickbird images and Aerial Photographs undergone through orthometric correction at the time of purchase. As result, from a comparison on positional accuracies of Orthoimages from KOMPSAT2 and Ortho Images of IKONOS was made. From the comparison the distance between the image points within each image and GPS surveying points was identified as 3.41m for KOMPSAT2 and as 1.45m for IKONOS, presenting a difference of 1.96m. Whereas, RMSE between image points was identified as 1.88m. The level of correlation was measured by using Quickbird, KOMPSAT2, IKONOS and Aerial Photographs between inter-image bands and NDVI, showing that there were high levels of correlation between Quickbird and IKONOS identified from all bands as well as from NDVI, except a high level of correlation that was identified between the Aerial Photographs and KOMPSAT2 from Band 2. Low levels of correlation were also identified between Quickbird and Aerial Photographs from Band 1. and between KOMPSAT2 and IKONOS from Band 2 and Band 4, whereas, KOMPSAT2 showed low correlations with Aerial Photographs from Band 3. For NDVI, KOMPSAT2 showed low level of correlations with both of QuickBird and IKONOS.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.4
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• pp.143-154
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• 2004

The researchers, who seek geological and environmental information, depend on the remote sensing and aerial photographic datum from various commercial satellites and aircraft. However, the adverse weather conditions and the expensive equipment can restrict that the researcher can collect their data anywhere and any time. To allow for better flexibility, we have developed a compact, a multi-spectral automatic Aerial photographic system(PKNU 2). This system's Multi-spectral camera can catch the visible(RGB) and infrared(NIR) bands($3032{\times}2008$ pixels) image. Visible and infrared bands images were obtained from each camera respectively and produced Color-infrared composite images to be analyzed in the purpose of the environment monitor but that was not very good data. Moreover, it has a demerit that the stereoscopic overlap area is not satisfied with 60% due to the 12s storage time of each data, while it was possible that PKNU 2 system photographed photos of great capacity. Therefore, we have been developing the advanced PKNU 2(PKNU 3) that consists of color-infrared spectral camera can photograph the visible and near infrared bands data using one sensor at once, thermal infrared camera, two of 40 G computers to store images, and MPEG board to compress and transfer data to the computer at the real time and can attach and detach itself to a helicopter. Verification and calibration of each sensor(REDLAKE MS 4000, Raytheon IRPro) were conducted before we took the aerial photographs for obtaining more valuable data. Corrections for the spectral characteristics and radial lens distortions of sensor were carried out.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.32-39
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• 2018

Raman spectra exhibit differences in intensity depending on the measuring equipment and environmental conditions even for the same material. This restricts the pattern recognition approach of Raman spectroscopy and is an issue that must be solved for the sake of its practical application, so as to enable the reusability of the Raman database and interoperability between Raman devices. To this end, previous studies assumed the existence of a transfer function between the measurement devices to obtain a direct spectral correction. However, this method cannot cope with other conditions that cause various intensity distortions. Therefore, we propose a classification method using linear intensity calibration which can deal with various measurement conditions more flexibly. In order to evaluate the performance of the proposed method, a Raman library containing 14033 chemical substances was used for identification. Ten kinds of chemical Raman spectra measured using three different Raman spectroscopes were used as the experimental data. The experimental results show that the proposed method achieves 100% discrimination performance against the intensity-distorted spectra and shows a high correlation score for the identified material, thus making it a useful tool for the identification of chemical substances.

• Journal of Korea Water Resources Association
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• v.57 no.1
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• pp.35-44
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• 2024

Research in dam inflow prediction has actively explored the utilization of data-driven machine learning and deep learning (ML&DL) tools across diverse domains. Enhancing not just the inherent model performance but also accounting for model characteristics and preprocessing data are crucial elements for precise dam inflow prediction. Particularly, existing rainfall data, derived from snowfall amounts through heating facilities, introduces distortions in the correlation between snow accumulation and rainfall, especially in dam basins influenced by snow accumulation, such as Soyang Dam. This study focuses on the preprocessing of rainfall data essential for the application of ML&DL models in predicting dam inflow in basins affected by snow accumulation. This is vital to address phenomena like reduced outflow during winter due to low snowfall and increased outflow during spring despite minimal or no rain, both of which are physical occurrences. Three machine learning models (SVM, RF, LGBM) and two deep learning models (LSTM, TCN) were built by combining rainfall and inflow series. With optimal hyperparameter tuning, the appropriate model was selected, resulting in a high level of predictive performance with NSE ranging from 0.842 to 0.894. Moreover, to generate rainfall correction data considering snow accumulation, a simulated snow accumulation algorithm was developed. Applying this correction to machine learning and deep learning models yielded NSE values ranging from 0.841 to 0.896, indicating a similarly high level of predictive performance compared to the pre-snow accumulation application. Notably, during the snow accumulation period, adjusting rainfall during the training phase was observed to lead to a more accurate simulation of observed inflow when predicted. This underscores the importance of thoughtful data preprocessing, taking into account physical factors such as snowfall and snowmelt, in constructing data models.